Junior Data Engineer – Growth Tracker

[ Name ] Junior Data Engineer – Growth Tracker

JDE  ·  SFIA 2-3  ·  raganmcgill.co.uk

1Novice
No evidence of this yet · Lacks experience in this competency · Requires significant training and guidance
2Developing
Evidence of trying but lacking consistency · Demonstrates effort and initial attempts · Progressing, consistency is needed
3Proficient
Evidence of doing this with areas for improvement · Competent with some areas for enhancement · Meets most expectations
4Accomplished
Evidence of consistently meeting expectations · Highly reliable in delivering results · Maintains performance standards
5Expert
Evidence of exceeding expectations · Demonstrates exceptional mastery · Autonomous · Leads and mentors others
Learning & Growth
Delivery
Quality & Craft
Communication
Collaboration
Ownership
Technical Foundation
Learning & Growth
Actively seeks to deepen understanding of data modelling, pipeline patterns, and the business domain they are serving.
Reflects on feedback from code reviews and applies lessons to subsequent work without being reminded.
Reads data engineering literature, follows community discussions, and brings relevant ideas back to the team.
Develops awareness of their own knowledge gaps and proactively raises them with their TTL or mentor.
Takes on moderately stretching tasks and uses them as development opportunities rather than defaulting to familiar approaches.
Builds understanding of the business context behind the data they work with - not just the technical pipeline.
Delivery
Delivers well-defined pipeline and data tasks independently within agreed timeframes.
Manages their own task queue effectively - breaking down work, estimating effort, and flagging when estimates change.
Raises blockers promptly with enough context for a senior to help efficiently.
Keeps PRs reviewable - appropriately scoped, with clear descriptions and evidence of testing.
Responds to review feedback promptly and addresses it thoroughly before requesting re-review.
Tracks task status accurately so the team always has a clear picture of progress.
Contributes to planning by providing thoughtful estimates and flagging risks they are aware of.
Quality & Craft
Writes SQL and Python that is clean, readable, and follows team conventions without needing to be reminded.
Applies appropriate data quality tests to all pipeline work - not just as box-ticking but as genuine protection for downstream consumers.
Reviews own work critically before submitting - checking for edge cases, null handling, and performance concerns.
Writes clear documentation for pipelines, transformations, and data models so that others can understand and maintain them.
Identifies and flags technical debt encountered during delivery work, even when not expected to resolve it immediately.
Develops growing instinct for pipeline performance - identifying queries that will not scale and raising concerns early.
Communication
Provides clear, specific stand-up updates that give teammates a genuine picture of progress and blockers.
Writes PR descriptions that explain what changed, why it changed, and how to verify the outcome.
Communicates data quality concerns clearly to senior engineers - with evidence, not just intuition.
Asks focused, well-formed questions that show prior investigation rather than asking before attempting.
Documents decisions and assumptions in pipelines and data models so future engineers understand the reasoning.
Gives constructive, specific feedback in code reviews on peers' work.
Collaboration
Builds effective working relationships with data analysts and business stakeholders to understand how data is consumed.
Contributes constructively to team ceremonies - retrospectives, planning, and technical discussions.
Offers meaningful code review feedback to graduate engineers, balancing rigour with encouragement.
Shares knowledge with teammates - new tools, useful patterns, lessons from debugging - without being asked.
Works openly rather than siloing work in progress - makes it easy for others to see and assist.
Engages positively with cross-team collaboration, treating other teams' needs with the same respect as the team's own.
Ownership
Takes full responsibility for completing tasks they have committed to, including follow-through on review actions.
Flags uncertainty about data requirements or business logic early rather than making assumptions.
Maintains data quality in areas they own - investigating alerts, fixing issues, and preventing recurrence.
Proactively monitors pipelines they have built or modified, not just during development but after deployment.
Owns their own learning progression and actively manages it, seeking feedback and opportunities.
Acknowledges mistakes clearly, investigates root causes, and shares learnings with the team.
Technical Foundation
Demonstrates solid SQL and Python proficiency in all delivered work.
Applies data quality testing practices consistently - tests are part of the definition of done, not an afterthought.
Uses the team's orchestration tool effectively - writing clear DAGs or workflow definitions with appropriate dependencies and error handling.
Understands dimensional modelling concepts well enough to contribute to data model design conversations.
Navigates the cloud data warehouse confidently - structuring efficient queries and understanding partitioning and clustering basics.
Understands the data lifecycle - ingestion, transformation, serving - and where their work fits within it.
Maintains working knowledge of the team's deployment and orchestration patterns.
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples
Evidence & examples

Strengths to recognise

Development focus areas

Overall assessment & agreed actions

raganmcgill.co.uk  ·  Junior Data Engineer Growth Tracker